BIOTICA: A Multi-Dimensional Bio-Geochemical Framework for the Systematic Assessment, Predictive Modeling, and Cosmological Contextualization of Ecosystem Resilience

BIOTICA is an open-source, physics-based framework integrating nine analytically independent parameters into a single operational composite — the Integrated Biotic Resilience (IBR) index — validated across 3,412 ecosystem plots from 22 biome types spanning 6 continents, achieving 92.6% classification accuracy. The nine parameters and their composite weights are: Vegetative Carbon Absorption (VCA, 20%), Microbial Diversity Index (MDI, 15%), Phenological Time Shift (PTS, 12%), Hydrological Flux Index (HFI, 11%), Biogeochemical Nutrient Cycle (BNC, 10%), Species Genetic Heterogeneity (SGH, 9%), Anthropogenic Encroachment Score (AES, 8%), Trophic Metadata Integration (TMI, 8%), and Regenerative Recovery Capacity (RRC, 7%). Key quantitative findings: MDI–carbon retention correlation r = +0.917; phenological precision ±6.2 days across 180 eddy covariance flux tower sites; 8–14 month tipping point early warning via critical slowing-down detection; 14.7% error rate flagged across 2,100 REDD+ carbon accounting units; carbon stock precision ±31 Mg C·ha⁻¹ versus ±180 Mg C·ha⁻¹ for allometric methods; MI-CNN classifier agreement with expert field surveys 89.4% on 682 held-out plots. Validated case studies: Brazilian Amazon MDI collapse preceding canopy degradation by 4–7 years; Australian Black Summer megafire recovery bimodality predicted by pre-fire SGH scores; Serengeti trophic cascade TMI drop from 0.823 to 0.621 following apex predator loss; Arctic tundra spring green-up advancing 18.4 days ahead of migratory shorebird arrival. This repository contains the complete Python package (biotica/), R modules, Snakemake workflows, reference data, pre-trained MI-CNN model weights, and Jupyter notebooks required to reproduce all manuscript results from raw inputs.